title: The prediction of type 1 diabetes by multiple autoantibody levels and their incorporation into an autoantibody risk score in relatives of type 1 diabetic patients.
abstract: OBJECTIVE
  We assessed whether a risk score that incorporates levels of multiple islet autoantibodies could enhance the prediction of type 1 diabetes (T1D).
  
  
  METHODS
  TrialNet Natural History Study participants (n = 784) were tested for three autoantibodies (GADA, IA-2A, and mIAA) at their initial screening. Samples from those positive for at least one autoantibody were subsequently tested for ICA and ZnT8A. An autoantibody risk score (ABRS) was developed from a proportional hazards model that combined autoantibody levels from each autoantibody along with their designations of positivity and negativity.
  
  
  RESULTS
  The ABRS was strongly predictive of T1D (hazard ratio [with 95% CI] 2.72 [2.23-3.31], P < 0.001). Receiver operating characteristic curve areas (with 95% CI) for the ABRS revealed good predictability (0.84 [0.78-0.90] at 2 years, 0.81 [0.74-0.89] at 3 years, P < 0.001 for both). The composite of levels from the five autoantibodies was predictive of T1D before and after an adjustment for the positivity or negativity of autoantibodies (P < 0.001). The findings were almost identical when ICA was excluded from the risk score model. The combination of the ABRS and the previously validated Diabetes Prevention Trial-Type 1 Risk Score (DPTRS) predicted T1D more accurately (0.93 [0.88-0.98] at 2 years, 0.91 [0.83-0.99] at 3 years) than either the DPTRS or the ABRS alone (P ≤ 0.01 for all comparisons).
  
  
  CONCLUSIONS
  These findings show the importance of considering autoantibody levels in assessing the risk of T1D. Moreover, levels of multiple autoantibodies can be incorporated into an ABRS that accurately predicts T1D.
authors.affiliation: Division of Endocrinology, University of Miami, Miami, Florida, USA. jsosenko@med.miami.edu
authors.forename: Jay M
authors.initials: JM
authors.lastname: Sosenko
authors.forename: Jay S
authors.initials: JS
authors.lastname: Skyler
authors.forename: Jerry P
authors.initials: JP
authors.lastname: Palmer
authors.forename: Jeffrey P
authors.initials: JP
authors.lastname: Krischer
authors.forename: Liping
authors.initials: L
authors.lastname: Yu
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Mahon
authors.forename: Craig A
authors.initials: CA
authors.lastname: Beam
authors.forename: David C
authors.initials: DC
authors.lastname: Boulware
authors.forename: Lisa
authors.initials: L
authors.lastname: Rafkin
authors.forename: Desmond
authors.initials: D
authors.lastname: Schatz
authors.forename: George
authors.initials: G
authors.lastname: Eisenbarth
mesh_terms: D000293:Adolescent; D000328:Adult; D001323:Autoantibodies / Q000276:immunology*; D002648:Child; D002675:Child, Preschool; D003922:Diabetes Mellitus, Type 1 / Q000276:immunology*; D005260:Female; D006801:Humans; D008297:Male; D012307:Risk Factors; D055815:Young Adult
chemical_list: D001323:Autoantibodies